Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zonglong Bai"'
Publikováno v:
IET Signal Processing, Vol 17, Iss 3, Pp n/a-n/a (2023)
Abstract Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are b
Externí odkaz:
https://doaj.org/article/85681eb9647140c39233b3cd5faf6113
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-19 (2021)
Abstract Estimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, including the platform size which put
Externí odkaz:
https://doaj.org/article/642600165b964d29b069c35a5abff8c9
Autor:
Zonglong Bai, Jinwei Sun
Publikováno v:
Computational Statistics.
Autor:
Zonglong Bai
Publikováno v:
Applied Acoustics. 207:109340
Publikováno v:
Bai, Z, Shi, L, Sun, J & Christensen, M G 2022, ' Space alternating variational estimation based sparse Bayesian learning for complex-value sparse signal recovery using adaptive Laplace priors ', IET Signal Processing . https://doi.org/10.1049/sil2.12179
Due to its self-regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3eefceda21b60da06f3a31862c76a67f
https://vbn.aau.dk/da/publications/6e34c889-95a1-4fa8-8177-c61575ad25ba
https://vbn.aau.dk/da/publications/6e34c889-95a1-4fa8-8177-c61575ad25ba
Publikováno v:
Bai, Z, Shi, L, Jensen, J R, Sun, J & Christensen, M G 2021, ' Acoustic DOA estimation using space alternating sparse Bayesian learning ', Eurasip Journal on Audio, Speech, and Music Processing, vol. 2021, no. 1, 14, pp. 1-19 . https://doi.org/10.1186/s13636-021-00200-z
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-19 (2021)
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-19 (2021)
Estimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoidrobots and drones. However, it is a most challenging problem due to a number of factors, including the platform sizewhich puts a constra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be80c64d3bffe35ce0d063c9c2bf6b1a
https://vbn.aau.dk/da/publications/9822b8e3-7129-4302-ab4c-d422129de704
https://vbn.aau.dk/da/publications/9822b8e3-7129-4302-ab4c-d422129de704
Publikováno v:
Applied Acoustics. 135:111-123
The functional link artificial neural network (FLANN) structure using trigonometric function expansion is used successfully in nonlinear active noise control (NANC) system. However, there are still two potential shortcomings to deteriorate its perfor
Publikováno v:
Bai, Z, Jensen, J R, Sun, J & Christensen, M G 2019, A Sparse Bayesian Learning Based RIR Reconstruction Method for Acoustic TOA and DOA Estimation . in 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) ., 8937087, IEEE, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 393-397, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics 2019, New Paltz, New York, United States, 20/10/2019 . https://doi.org/10.1109/WASPAA.2019.8937087
WASPAA
WASPAA
Acoustic reflector estimation, which is one of the key problems of robot audition, is addressed in this paper using a sparse Bayesian learning (SBL) approach. More specifically, we propose a threestep procedure in which we 1) reconstruct the room imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9a85af30dbfbbbe8c7579309422e361
https://vbn.aau.dk/da/publications/26437736-eb2f-4667-9776-3a1299cf2632
https://vbn.aau.dk/da/publications/26437736-eb2f-4667-9776-3a1299cf2632
Autor:
Zonglong Bai, Vicent Molés-Cases, Mads Græsbøll Christensen, Gema Piñero, Alberto Gonzalez, Jesper Rindom Jensen
Publikováno v:
Bai, Z, Sun, J, Jensen, J R & Christensen, M G 2019, Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data . in 2019 27th European Signal Processing Conference (EUSIPCO) ., 8903069, IEEE, Proceedings of the European Signal Processing Conference, 27th European Signal Processing Conference, EUSIPCO 2019, Coruña, Spain, 02/09/2019 . https://doi.org/10.23919/EUSIPCO.2019.8903069
EUSIPCO
EUSIPCO
In this paper, the problems of indoor sound sourcelocalization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm isproposed. Using time delays for the direct paths from candidate source location
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23221fec2be7271cd0eb4624d68647ac
https://vbn.aau.dk/da/publications/fae08469-2618-4f7b-82e9-a4dcdfc85eb0
https://vbn.aau.dk/da/publications/fae08469-2618-4f7b-82e9-a4dcdfc85eb0
Publikováno v:
2017 3rd International Conference on Control, Automation and Robotics (ICCAR).
The feedback Active Noise Control (ANC) is mainly used to reduce tonal noise, but for broadband noise like chaotic noise, it is less involved other than using filtered-s least mean square (FSLMS) algorithm. Though the feedback ANC based on FSLMS algo